This book helps to meet the challenge of managing and using Big Data by presenting new research on various technological advances in the field.
Data has become a valuable asset like never before. Today the challenge is not a shortage of data but the need for techniques and methods capable enough to be able to glean valuable insights from the fast-flowing mass of Big Data. This new volume, Handbook of Research for Big Data: Concepts and Techniques, helps to meet the challenge of managing and using Big Data by presenting new research on various technological advances in the field.
The chapters in the book present information on important applications, concepts, and technologies for Big Data in the present industry and market scenario. It looks at research domain issues and their solutions as well as various research case studies, research plans, methodologies, and related data sets for the four Vs: volume, velocity, variety, and veracity.
Chapters discuss Big Data in governance, transportation, disaster management, epidemiology, and more. The book covers design and analysis of reconfigurable computing of SoC for IoT, data mining techniques and applications, the use of natural language processing in big data, and more.
The volume is a valuable resource for researchers from both academia and industry to learn about and enhance their knowledge and skills in the broad area of big data computing and applications.
1. Big Data in Governance in India: Case Studies
2. Design and Analysis
of Reconfigurable Computing of SoC for IoT Applications
3. A Review of
Different Data Mining Techniques Used in Big Data Applications
4. Big Data
Applications in Transportation Systems Using the Internet of Things
5.
Overview of Big Data and Natural Language Processing: A Powerful Combination
for Research
6. An Insight into Big Data and Its Pertinence
7. Big Data
Science: Models and Approaches, Characteristics, Challenges, and Applications
8. Conceptual Frameworks for Big Data Visualization: Discussion on Models,
Methods, and Artificial Intelligence for Graphical Representations of Data
9.
Role of Machine Learning in Big Data Peregrination
10. Artificial Neural
Networks: Fundamentals, Design, and Applications
11. Big Data: Trends,
Challenges, Opportunities, Tools, Success Factors, and the Way Toward
Pandemic Analytics
12. Applying Tenacious Machine Learning Classification
Techniques for Drug-Free Nipah Virus Prediction
13. An Approach for
Controlling Disaster Management by Machine Learning Technique
Brojo Kishore Mishra, PhD, is Professor in the Computer Science and Engineering Department at the Gandhi Institute of Engineering and Technology University, Odisha, India. He has published research papers in national and international conference proceedings and peer-reviewed journals as well as book chapters and several authored and edited books. His research interests include data mining and big data analysis, machine learning, soft computing, and evolutionary computation.
Vivek Kumar, PhD, is a Marie Sklodowska-Curie researcher with the University of Cagliari, Italy, and Philips Research, the Netherlands. He formerly worked as a research engineer for a SHiP (search for hidden particles) project of CERN (European Organization for Nuclear Research) and the National University of Science and Technology (MISiS). He has also rendered his services to the Embassy of India in Moscow to the education and defense sector for strengthening Indo-Russian bilateral relations. He is a member of the Defense and Security Software Engineers Association, Italy. He has authored several publications and is also reviewer and editor of several journals in his field.
Sanjaya Kumar Panda, PhD, is Assistant Professor and Head of the Department of Computer Science and Engineering at the Indian Institute of Information Technology, Design and Manufacturing, Andhra Pradesh, India. He formerly worked as Assistant Professor in the Department of IT at Veer Surendra Sai University of Technology, Odisha, India. He has published more than 60 papers in reputed journals and conferences and is a member of many professional organizations. He has delivered several invited talks and has chaired sessions at many national and international conferences and workshops.
Prayag Tiwari, PhD, is a Marie Sklodowska Curie researcher with the University of Padua, Italy. He has teaching and industrial work experience and was previously a Research Assistant with the National University of Science and Technology (MISiS). He has several publications in journals, book series, and conferences. His research interests include machine learning, deep learning, quantum inspired machine learning, and information retrieval.
